{"cells":[{"cell_type":"code","source":["#%%capture\n","!pip install transformers==4.29.0  peft datasets==2.12.0"],"metadata":{"id":"onTs33D8r8x2"},"id":"onTs33D8r8x2","execution_count":null,"outputs":[]},{"cell_type":"code","source":["#pip install peft"],"metadata":{"id":"FQo2BljWk_Ll"},"id":"FQo2BljWk_Ll","execution_count":null,"outputs":[]},{"cell_type":"code","source":["!pip list |egrep transformers\n","!pip list |egrep peft\n","!pip list |egrep ^datasets\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nXFwrtm_TABK","executionInfo":{"status":"ok","timestamp":1692294280889,"user_tz":-180,"elapsed":3405,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"0755b843-0876-4409-bf88-bffb746d0d32"},"id":"nXFwrtm_TABK","execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["transformers                     4.29.0\n","peft                             0.4.0\n","datasets                         2.12.0\n"]}]},{"cell_type":"code","execution_count":3,"id":"5f93b7d1","metadata":{"id":"5f93b7d1","executionInfo":{"status":"ok","timestamp":1692294288978,"user_tz":-180,"elapsed":8096,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"outputs":[],"source":["from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n","from transformers import (default_data_collator,\n","                          get_linear_schedule_with_warmup)\n","from peft import (get_peft_config,\n","                  get_peft_model,\n","                  get_peft_model_state_dict,\n","                  LoraConfig,\n","                  TaskType)\n","import torch, os\n","from torch.utils.data import DataLoader\n","from tqdm import tqdm\n","import pandas as pd\n","from datasets import (load_dataset,\n","                      Dataset, DatasetDict)"]},{"cell_type":"markdown","source":["# Loading SNLI dataset from huggingface dataset hub"],"metadata":{"id":"lZCKJEpMMURo"},"id":"lZCKJEpMMURo"},{"cell_type":"code","source":["dataset = load_dataset(\"snli\")\n","snli_sampled=pd.DataFrame(dataset[\"train\"])\n","snli_sampled=snli_sampled.sample(frac=0.01, random_state=123)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":329,"referenced_widgets":["e5dd275fb3374ad78751637b98c817c8","d2dc3e6e7c2449b69a6a548892526ef0","eddf8a92d08945489b483c4a22fc95e2","f1af030796614fafb4e39fc5c3411df8","c0d5ea491e184345bf18cefcd381a161","4bb43890881743e99d301ff8a87c026b","cae4739a79bf4d02a1017bb755a9579c","0696fb311861496a82f9b835280d2c1d","7c69f1af82c743dc99f27149e55f684a","ac5b91ef2e92436fbc253740e9c637a7","a17b5206b10c4885b6d9740fbf721d98","b7bc2ef9e0d04c4794674b1c402b502e","69a71d000d54483ebc1b0c94dc5069ed","99fab5cab9e5400499183fa0fdac6222","0cf4f65ef954423096a525ce300368cf","a8df5a5dbd034eaeae264f8936cf0220","a3afacb67f4b4d99838a059e5d2402ab","5aca138fd0ed459f8cf4d0f76fe27359","56e2d88cb3464d8586a1d2da733f03b1","d6e5e18bce0e4623a1638df26b862987","7bf2f90fab7d4aa2a74976da0a0bc86c","6bb63671acf84a06aeaf5e8fcefe4c91","98bb1b6a28ff4afe923e4452a51640ad","96f2507e2583491b85a8fb13f847cb1d","a6685d1782014357a6ba8a07a5e2f229","35f5faa2a7bc4381a44f1244f61762e0","4e4a84b9f9cf499f8dd85f5001e20df8","4ecfd3a038f645ae96df649b4a29022d","bc6afab57037471f88585393e175b1f5","22e3eb3b62924eeca49548528d3d6b00","0487484deffc41d790986570f96bc72e","92a38244caf74927999d2cc3bc1e64ab","e94630f809934903b14e230b5b5aba2f","45bbc7afd5274aebb37e29671ffdb25e","c8952541a0ce48858d619b564b66f889","c855d6cb24ec428d98255024df595b5b","52b3b0be52314798bf67685f91598859","357a4d7f5a324488b289e4d1b84e323b","f41532b295e04e6d8f195ca50c2b145e","079377ca384943cfa0de43e630d99119","d47cdac4c2454251a3aa6fe5eb2da1b8","de886bcf36b04eae92579c89b4ab50f5","c934832fa1dc4c6299e9753c175577a8","42fec26b55674fd0916372dd2d860b81","439507f795ed47809a538f879364fe0f","27b1acb254c84aab8d061e1922c1d6dc","2370c800053148a4b78bfb7a0afe747f","4556c59c91d143f88c3b47318deffecd","3cb8a13ac13f4fa4bf1d9e5fa1e8a444","f552826938254422949f105958ffd463","e304068ee93843128aea47ac47b88e75","dd771e0c136e436ebc4adba28390cc58","790d38b5204a401d8586fedb4962eb33","086ded0444b5403f91bdeb6a0d7a407f","7ab5608b3c9f444fab2cd770a1339de5","640a2c25e4c24c0a8baa2a017be4532d","9241f25a88da48cbaf984dd8e2175265","837c551a7f6f4a3f82474ef89616272a","aa7a8945bb0644cb9dcc6221d42ac70a","1c3e353bdeb9488a87356ca52d71897f","f3077b5967b448288471d88a3e2a341a","7bcd2f49df204725be9d6031282a4b6b","dda1d6670bf24c5da8bfa7f3a69f9ea3","cf17a20841254ece8df6bec37f159791","2641d72bcc9b46a6ac6bd88b22dda542","dd7ff9b367e946f3aed23262e21df7b8","7268aa049968423898394dceaf2af8b3","424b1a163dd8495fbb52ae49a938293d","647506d198934bb09264623f1a740ad5","ab09410f133347cc9cdf3cf7fa267a63","0e742127df0945bf95dea0d44f6ef1d4","d7742628f47b40b1a464ce9e0e3dab8c","0f83bd0e1099463eadfabc0e006757db","474f821d98764e10a71ad73def3b98b1","975b8ad9b42144b58752a9d37299d601","3a3834ae6ae14b5885c2060988028a5e","31aaabc785b24f819572d136cd95038a","cd02a19531fe4377bc292d45982ac3f1","25e20e51002c4cc68d616ea95de5e7d9","d71ad1cf55044ea2bee81c4d1b9f559c","61e2e7ab25c84c05ae3efa3d78deb6e0","e095a78baef5413db16bcfb20ddc3490","17042c75d544402ea9c4e4b1f7556432","95dbd01cd7d1471fb1bc03b25217c7b9","334d5138ec3c43949872d931bb28c029","eb5d04d1dcf14f8db285b09456e93dc7","8f4f3c05a9174f6ca04b9656812b1146","f522db3b2cb640719a61c05fbcfb67e0"]},"id":"GLCU6GVN-pBC","executionInfo":{"status":"ok","timestamp":1692294328232,"user_tz":-180,"elapsed":39270,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"d208b790-98e7-430d-ac55-96d13e8edf31"},"id":"GLCU6GVN-pBC","execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/3.82k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e5dd275fb3374ad78751637b98c817c8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/1.90k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b7bc2ef9e0d04c4794674b1c402b502e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/14.1k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"98bb1b6a28ff4afe923e4452a51640ad"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset snli/plain_text to /root/.cache/huggingface/datasets/snli/plain_text/1.0.0/1f60b67533b65ae0275561ff7828aad5ee4282d0e6f844fd148d05d3c6ea251b...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.93k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"45bbc7afd5274aebb37e29671ffdb25e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.26M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"439507f795ed47809a538f879364fe0f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/65.9M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"640a2c25e4c24c0a8baa2a017be4532d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.26M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7268aa049968423898394dceaf2af8b3"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset snli downloaded and prepared to /root/.cache/huggingface/datasets/snli/plain_text/1.0.0/1f60b67533b65ae0275561ff7828aad5ee4282d0e6f844fd148d05d3c6ea251b. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cd02a19531fe4377bc292d45982ac3f1"}},"metadata":{}}]},{"cell_type":"code","source":["snli_sampled.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rJfgeX6z_Nw1","executionInfo":{"status":"ok","timestamp":1692294328234,"user_tz":-180,"elapsed":79,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"3143b4b3-1327-4c38-ec7e-43b8120538cc"},"id":"rJfgeX6z_Nw1","execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(5502, 3)"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["snli_sampled.label.value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4A8do1hj-mcs","executionInfo":{"status":"ok","timestamp":1692294328235,"user_tz":-180,"elapsed":72,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"17c75ca6-5f59-4438-a90d-9c6ee1797e58"},"id":"4A8do1hj-mcs","execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" 2    1866\n"," 0    1828\n"," 1    1803\n","-1       5\n","Name: label, dtype: int64"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["snli_sampled= snli_sampled[snli_sampled.label>-1]"],"metadata":{"id":"laoIiiLp_TBk","executionInfo":{"status":"ok","timestamp":1692294336185,"user_tz":-180,"elapsed":324,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"id":"laoIiiLp_TBk","execution_count":7,"outputs":[]},{"cell_type":"code","source":["names=dataset[\"train\"].features[\"label\"].names\n","names"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0DKTCDX6_S_B","executionInfo":{"status":"ok","timestamp":1692294336520,"user_tz":-180,"elapsed":3,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"267b6a82-6661-484f-fbf8-627e43866925"},"id":"0DKTCDX6_S_B","execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['entailment', 'neutral', 'contradiction']"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["mapp=dict(enumerate(names))\n","mapp"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UDEVPadF_S8Z","executionInfo":{"status":"ok","timestamp":1692294336924,"user_tz":-180,"elapsed":7,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"491fa552-54d1-4106-9245-06c6f6a7bfd8"},"id":"UDEVPadF_S8Z","execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0: 'entailment', 1: 'neutral', 2: 'contradiction'}"]},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","source":["## Preparing dataset for training"],"metadata":{"id":"SwXwnhgCOF_m"},"id":"SwXwnhgCOF_m"},{"cell_type":"code","source":["snli_sampled_df= pd.DataFrame(snli_sampled)\n","snli_sampled_df[\"text\"]= snli_sampled_df\\\n","      .apply(lambda x: \"S1:\" +x.premise\n","             +\" S2:\"+x.hypothesis+\n","             \". The relation between S1 and S2 is labeled \"+\n","             \"as entailment, neutral or contradiction ?\",\n","            axis=1)\n","snli_sampled_df[\"label\"]=snli_sampled_df\\\n","        .apply(lambda x: f\"It is {mapp[x.label]}\",\n","               axis=1)"],"metadata":{"id":"dellBc6o_S58","executionInfo":{"status":"ok","timestamp":1692294339542,"user_tz":-180,"elapsed":397,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"id":"dellBc6o_S58","execution_count":10,"outputs":[]},{"cell_type":"code","source":["snli_sampled_df.head(3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"YiYgx9CAFMCj","executionInfo":{"status":"ok","timestamp":1692294340988,"user_tz":-180,"elapsed":15,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"144d2174-9c6d-4c51-cac5-f7047019c51f"},"id":"YiYgx9CAFMCj","execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                  premise  \\\n","190268  Two firefighters clad in protective gear are e...   \n","300525   Two men work together on a construction project.   \n","465851             Three men in uniform walk around town.   \n","\n","                                    hypothesis                label  \\\n","190268  Two firefighters are entering a house.     It is entailment   \n","300525                    Two men are working.     It is entailment   \n","465851            Three men rob the residents.  It is contradiction   \n","\n","                                                     text  \n","190268  S1:Two firefighters clad in protective gear ar...  \n","300525  S1:Two men work together on a construction pro...  \n","465851  S1:Three men in uniform walk around town. S2:T...  "],"text/html":["\n","  <div id=\"df-445d60a5-4f26-406f-b72c-2aefce5ae278\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>premise</th>\n","      <th>hypothesis</th>\n","      <th>label</th>\n","      <th>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>190268</th>\n","      <td>Two firefighters clad in protective gear are e...</td>\n","      <td>Two firefighters are entering a house.</td>\n","      <td>It is entailment</td>\n","      <td>S1:Two firefighters clad in protective gear ar...</td>\n","    </tr>\n","    <tr>\n","      <th>300525</th>\n","      <td>Two men work together on a construction project.</td>\n","      <td>Two men are working.</td>\n","      <td>It is entailment</td>\n","      <td>S1:Two men work together on a construction pro...</td>\n","    </tr>\n","    <tr>\n","      <th>465851</th>\n","      <td>Three men in uniform walk around town.</td>\n","      <td>Three men rob the residents.</td>\n","      <td>It is contradiction</td>\n","      <td>S1:Three men in uniform walk around town. S2:T...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-445d60a5-4f26-406f-b72c-2aefce5ae278')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-445d60a5-4f26-406f-b72c-2aefce5ae278 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-d0d0e767-b1d9-4bb5-8828-436371059b0d\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d0d0e767-b1d9-4bb5-8828-436371059b0d')\"\n","            title=\"Suggest charts.\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const charts = await google.colab.kernel.invokeFunction(\n","          'suggestCharts', [key], {});\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-d0d0e767-b1d9-4bb5-8828-436371059b0d button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["CUT=snli_sampled_df.shape[0]*7//10\n","print(f\"Training set size is {CUT}\")\n","print(f\"Validation set size is \\\n","        {snli_sampled_df.shape[0]-CUT}\")\n","print(f\"Total size is {snli_sampled_df.shape[0]}\")\n","\n","snli_sampled_dict= DatasetDict({\"train\":\n","                         Dataset.from_pandas(snli_sampled_df[:CUT]),\n","                        \"validation\":\n","                         Dataset.from_pandas(snli_sampled_df[CUT:]),\n","                        })"],"metadata":{"id":"eLON7CJh_S3S","executionInfo":{"status":"ok","timestamp":1692294341474,"user_tz":-180,"elapsed":5,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"d5c69bb2-88f7-44fe-edbe-3a774bb4b181"},"id":"eLON7CJh_S3S","execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["Training set size is 3847\n","Validation set size is         1650\n","Total size is 5497\n"]}]},{"cell_type":"code","source":["def preprocess_function(examples):\n","    inputs = examples[\"text\"]\n","    targets = examples[\"label\"]\n","    model_inputs = tokenizer(inputs, max_length=max_length,\n","                             padding=\"max_length\",\n","                             truncation=True,\n","                             return_tensors=\"pt\")\n","    labels = tokenizer(targets,\n","                       max_length=max_target_len,\n","                       padding=\"max_length\",\n","                       truncation=True,\n","                       return_tensors=\"pt\")\n","    labels = labels[\"input_ids\"]\n","    labels[labels == tokenizer.pad_token_id] = -100\n","    model_inputs[\"labels\"] = labels\n","    return model_inputs"],"metadata":{"id":"wpHG6yhdAXDW","executionInfo":{"status":"ok","timestamp":1692294342797,"user_tz":-180,"elapsed":2,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"id":"wpHG6yhdAXDW","execution_count":13,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"qNcLBqxAk0RX","executionInfo":{"status":"ok","timestamp":1692294343881,"user_tz":-180,"elapsed":2,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"id":"qNcLBqxAk0RX","execution_count":13,"outputs":[]},{"cell_type":"code","source":["model_name_or_path=\"google/flan-t5-base\" # 250M parameters\n","#model_name_or_path=\"google/flan-t5-large\" # 780M parameters\n","#model_name_or_path=\"google/flan-t5-xl\" # 3B parameters\n","tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)"],"metadata":{"id":"hFdbH42kkige","executionInfo":{"status":"ok","timestamp":1692294347043,"user_tz":-180,"elapsed":2799,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["565e16a9a7ce4d2da040a4d7cfe102d3","cb26e6bdb7574e7690225979373eb37a","85a1548743734342b491da566073c7d0","fc1f1250098a412e9d6b781d0d36d191","e9ef1e267f6f476b8bfeb223506f03c4","fc39cb267eeb453aab05681df3bc00ac","ef4ff7c9fbfe4156b7dbc3023fb7dc8a","d213ee108667499ea7a26678282af664","bd6578c1acd445f2a315a04497322ded","d7f4e3d45ad041d089941224d5df4304","14a0b35aed604708a5b407e21ccd2eef","24e903d5d2a24c84b2d65ad6635044dd","ccb98fcfab684dcf81935c1656ed6735","84061e0698e34c7887a36f9380863b29","b76b097dbb8040eeb83ea9beb9c8659c","172542268fdc4fecbb26477b77226f68","d4720825f0a54bb1b621587204e4e8b2","b96a2e2e607e437ba7490aeae9fb4600","fef5eccdeab9497988cbea792750d8ca","27012160ece7435a8a90bea11d5dcb4a","01e9b4a77aef4c1dbafe5631a565cbae","68f1df0d5a9445d0a47d531c915925f9","0699789bf6da493387a596fddb0e9362","64c491c24d544b4ba588e32f34fbf728","48f04fc92d6548ce9ec93dc44f44a8ac","16c278e68bc44d0bb020c9cdb3cb6804","d8bac1484e59406e90890174aadac276","05733d175493493ba0180262716ca85c","1cddfe626afd4309b78920d0791c129e","d82a2da6510f4f0dbefc1a16acc02fb1","4ffa53863b4d46658ee6f24ff4c4c133","531c42f315ab49edbf13243a5b8ff1df","a5d5f1a28ba54c5d8486f924ec811a65","162c4947ae12408a8e37c469981bea6b","fcc33921c43c4aadb1746415b78ea405","4060503b63a94b7f811640788a856b07","84836edcc8d74a018abb73e00ce3c765","f2241c8f462244ba8f13d344d121f272","01006e2f34fe4581aa5302d46e166c3e","a339037bc17e48c1ba9ab056c4634b32","3b240b3f7a064715a6813ce56953c9ea","8d9c23e9f231448f981d3e34f53460ca","33ab99444106428abedf42ea3c94bc62","ed5676bf1d014d8dbac0883c239a3736"]},"outputId":"6f84c7eb-7e75-4f68-ffe8-8dff66060374"},"id":"hFdbH42kkige","execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"565e16a9a7ce4d2da040a4d7cfe102d3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"24e903d5d2a24c84b2d65ad6635044dd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0699789bf6da493387a596fddb0e9362"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"162c4947ae12408a8e37c469981bea6b"}},"metadata":{}}]},{"cell_type":"code","source":["max_length = 150\n","max_target_len=10\n","\n","snli_processed = snli_sampled_dict.map(\n","    preprocess_function,\n","    batched=True,\n","    num_proc=1,\n","    remove_columns=snli_sampled_dict[\"train\"].column_names,\n","    load_from_cache_file=False,\n","  )\n","train_dataset = snli_processed[\"train\"]\n","eval_dataset = snli_processed[\"validation\"]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17,"referenced_widgets":["0af7b2ed23574faba05403bf7c5cf590","9f370cf40822408d80d7f518d35cff4d","d8835977f35b4e0ba9d1587b821a3b9e","3c7d9b81a5a04c93854c7a4436f3ba38","51ba2bc04ecd4d8aa830525cc6e1f523","3cbee720c347435e96ff41f56d6d1706","52c3149fabef42c2b8a62b0a044240a6","4e2aba2d3e0b41c78151f9b97c5b611f","d0571889a22c454bb79f2def6af12369","632e359fadf048d8be1eb96a009a6df4","163e15b680e3491eb7b3519596fac890","fe9411f218354bc6810945455cd3eff8","d4d77097d1c84c5e846a2fca484df3db","61e4279bff13473abd4dcffb4a298612","6b52418fac944f45847ee2328276c49e","42917bc250e84f38924dc164f6ff7f88","cfc68f057d26419d84230e7ae5cb8126","654565fd223d4e7ead945ff3ebe7789f","961fe963fabd488b943dc165a3d0000d","2754973dfaea457db96a531b60132873","8e9151ef1e004fac9ac5cdbf05f3d4eb","30ec980201314edca38dfcd65b93f419"]},"id":"DXpKZLRB_Svs","executionInfo":{"status":"ok","timestamp":1692294347844,"user_tz":-180,"elapsed":817,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"6a689fda-3b18-4caf-ec8d-10be13acf4db"},"id":"DXpKZLRB_Svs","execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/3847 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0af7b2ed23574faba05403bf7c5cf590"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1650 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fe9411f218354bc6810945455cd3eff8"}},"metadata":{}}]},{"cell_type":"code","source":["pd.DataFrame(train_dataset).head(3)"],"metadata":{"id":"5IbOwEYmGdvv","colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"status":"ok","timestamp":1692294348759,"user_tz":-180,"elapsed":922,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"7ae6ff82-8271-4b0c-8298-5a4a51baad02"},"id":"5IbOwEYmGdvv","execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                           input_ids  \\\n","0  [180, 536, 10, 382, 210, 32, 29764, 3, 4651, 2...   \n","1  [180, 536, 10, 382, 210, 32, 1076, 161, 544, 3...   \n","2  [180, 536, 10, 11889, 15, 15, 1076, 16, 7117, ...   \n","\n","                                      attention_mask  \\\n","0  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n","1  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n","2  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n","\n","                                              labels  \n","0    [94, 19, 3, 35, 5756, 297, 1, -100, -100, -100]  \n","1    [94, 19, 3, 35, 5756, 297, 1, -100, -100, -100]  \n","2  [94, 19, 27252, 1, -100, -100, -100, -100, -10...  "],"text/html":["\n","  <div id=\"df-5a017483-6a41-4d5c-b6c6-0e570fa36824\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>input_ids</th>\n","      <th>attention_mask</th>\n","      <th>labels</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>[180, 536, 10, 382, 210, 32, 29764, 3, 4651, 2...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n","      <td>[94, 19, 3, 35, 5756, 297, 1, -100, -100, -100]</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>[180, 536, 10, 382, 210, 32, 1076, 161, 544, 3...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n","      <td>[94, 19, 3, 35, 5756, 297, 1, -100, -100, -100]</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>[180, 536, 10, 11889, 15, 15, 1076, 16, 7117, ...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n","      <td>[94, 19, 27252, 1, -100, -100, -100, -100, -10...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a017483-6a41-4d5c-b6c6-0e570fa36824')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-5a017483-6a41-4d5c-b6c6-0e570fa36824 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-29cd8789-10ab-439f-bade-8652d330b7ec\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-29cd8789-10ab-439f-bade-8652d330b7ec')\"\n","            title=\"Suggest charts.\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const charts = await google.colab.kernel.invokeFunction(\n","          'suggestCharts', [key], {});\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-29cd8789-10ab-439f-bade-8652d330b7ec button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["batch_size = 32\n","train_dataloader = DataLoader(\n","    train_dataset,\n","    shuffle=True,\n","    collate_fn=default_data_collator,\n","    batch_size=batch_size,\n","    pin_memory=True\n",")\n","eval_dataloader = DataLoader(\n","    eval_dataset,\n","    collate_fn=default_data_collator,\n","    batch_size=batch_size,\n","    pin_memory=True)"],"metadata":{"id":"FKmTJORu-lcJ","executionInfo":{"status":"ok","timestamp":1692294349275,"user_tz":-180,"elapsed":522,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"id":"FKmTJORu-lcJ","execution_count":17,"outputs":[]},{"cell_type":"markdown","source":["# Initilizing and Training PEFT model with LORA"],"metadata":{"id":"QNM5kx3_OPme"},"id":"QNM5kx3_OPme"},{"cell_type":"code","source":["# creating model with peft\n","# if you want to train the pipline in vanilla mode, set with_peft=False\n","with_peft=True\n","model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n","\n","lr=2e-5\n","if with_peft:\n","  lr=1e-3\n","  peft_config = LoraConfig(task_type=TaskType.SEQ_2_SEQ_LM,\n","                           inference_mode=False,\n","                           r=8,\n","                           lora_alpha=32,\n","                           lora_dropout=0.1,\n","                           target_modules=['q', 'v']\n","                           )\n","  model = get_peft_model(model, peft_config)\n","  model.print_trainable_parameters()"],"metadata":{"id":"UUiNdc6LwwxV","colab":{"base_uri":"https://localhost:8080/","height":131,"referenced_widgets":["5187634aa69e4947b2ef8f021584b670","78a6dad029c0456a86e8706f453d34e3","8ac3e0bde59d41ceab98ca8de664faa1","e5a3f350b30e4143b681959c13691945","c53107b2668a4852b64680a931ca1efd","e8e147ba9f9a4ba982a9a90d9a3b32ca","f4756617537749dda8c5826ec4047f6f","bd850bd432ce4c0fae4ef544848d2d15","fa0eb045fd7a4ede9e3f349a15e8373c","0dc9240223cd4ada8ad1179019e047c6","5d2f56e011c043ac9533e8c56745301b","d486193811734146b3406ad86a79d49c","ff22e11f71504002a83edc0006a023b8","fff9cf9d5e5049229ac0b9ceecab8c56","c57ca597e73b4d949f76f198c131d4df","34fe126945524f0fa38d469b76112ab2","af82b2639bf34fa8881f110aec2b473d","7b4b15f422f04ee88a17285e9f3edd45","d49c3c534793420cb9f55d4ed7e530b0","50345a7741c748e8815aec267b3ceca3","9a57b3ff236341d89bfda74644fcd143","7e38ee0dcdb7488dbb4c004790eb611f","ae164127e9074dcd8d00933bbea879ef","93aff85e748d4bc5b05da99bcbb875f9","c493f9f1a9cd405ca5e19b8ece2bada9","aca7dd2f7cb54a3ba6a5c54e22897077","ca1401d3547e491bb3f2d83744599f09","5b90140e603e4b889b7c9d8073a96b5a","189de2dccaae42c087c7c62c54b28aa8","d6dc50bd42d74f3080768d273516f64d","8967bc7afbbd4a9aa7c7829bbd24d864","4d6a77a4b1d9490ca1fd33b20603850c","74afe1e5863a4242a6de197c04a085ae"]},"executionInfo":{"status":"ok","timestamp":1692294358384,"user_tz":-180,"elapsed":6624,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"0e8649b9-9d5f-4ecc-effc-d02e89205ea5"},"id":"UUiNdc6LwwxV","execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5187634aa69e4947b2ef8f021584b670"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading model.safetensors:   0%|          | 0.00/990M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d486193811734146b3406ad86a79d49c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ae164127e9074dcd8d00933bbea879ef"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["trainable params: 884,736 || all params: 248,462,592 || trainable%: 0.3560841867092814\n"]}]},{"cell_type":"code","source":["# peft + t5 icin  target_modules=['q', 'v'] -> denenecek"],"metadata":{"id":"RmpXL5lPG7ag","executionInfo":{"status":"ok","timestamp":1692294358386,"user_tz":-180,"elapsed":9,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"id":"RmpXL5lPG7ag","execution_count":19,"outputs":[]},{"cell_type":"code","execution_count":20,"id":"6b3a4090","metadata":{"id":"6b3a4090","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1692294457846,"user_tz":-180,"elapsed":99467,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"17128e4d-849f-4c4e-e4ad-b95724a8ec27"},"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 121/121 [00:28<00:00,  4.30it/s]\n","100%|██████████| 52/52 [00:05<00:00, 10.17it/s]\n"]},{"output_type":"stream","name":"stdout","text":["epoch=0-> train_loss_avg=tensor(0.3204, device='cuda:0')\t eval_loss_avg=tensor(0.0838, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 121/121 [00:25<00:00,  4.83it/s]\n","100%|██████████| 52/52 [00:05<00:00, 10.18it/s]\n"]},{"output_type":"stream","name":"stdout","text":["epoch=1-> train_loss_avg=tensor(0.1036, device='cuda:0')\t eval_loss_avg=tensor(0.0818, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 121/121 [00:25<00:00,  4.83it/s]\n","100%|██████████| 52/52 [00:05<00:00, 10.17it/s]"]},{"output_type":"stream","name":"stdout","text":["epoch=2-> train_loss_avg=tensor(0.0949, device='cuda:0')\t eval_loss_avg=tensor(0.0797, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}],"source":["device=\"cuda\"\n","model = model.to(device)\n","num_epochs = 3\n","\n","# optimizer and lr scheduler\n","optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n","lr_scheduler = get_linear_schedule_with_warmup(\n","    optimizer=optimizer,\n","    num_warmup_steps=0,\n","    num_training_steps=(len(train_dataloader) * num_epochs),\n",")\n","\n","import time\n","st = time.time()\n","for epoch in range(num_epochs):\n","    model.train()\n","    total_loss = 0\n","    for step, batch in enumerate(tqdm(train_dataloader)):\n","        batch = {k: v.to(device) for k, v in batch.items()}\n","        outputs = model(**batch)\n","        loss = outputs.loss\n","        total_loss += loss.detach().float()\n","        loss.backward()\n","        optimizer.step()\n","        lr_scheduler.step()\n","        optimizer.zero_grad()\n","\n","    model.eval()\n","    eval_loss = 0\n","    eval_preds = []\n","    for step, batch in enumerate(tqdm(eval_dataloader)):\n","        batch = {k: v.to(device) for k, v in batch.items()}\n","        with torch.no_grad():\n","            outputs = model(**batch)\n","        loss = outputs.loss\n","        eval_loss += loss.detach().float()\n","        eval_preds.extend(\n","            tokenizer.batch_decode(\n","                torch.argmax(outputs.logits, -1)\\\n","                .detach().cpu().numpy(),\n","                skip_special_tokens=True)\n","        )\n","    eval_loss_avg = eval_loss / len(eval_dataloader)\n","    train_loss_avg = total_loss / len(train_dataloader)\n","    print(f\"{epoch=}-> {train_loss_avg=}\\t {eval_loss_avg=}\")\n","et = time.time()\n","elapsed_time = et - st"]},{"cell_type":"code","source":["zipped=zip(eval_preds, snli_sampled_dict[\"validation\"][\"label\"])\n","q=[real.strip() in pred.strip() for pred,real in zipped]\n","print(f\"{model_name_or_path=}\")\n","print(f\"{num_epochs=}\")\n","print(f\"{elapsed_time=:.2f} seconds\"\n","     + (\" with PEFT\" if with_peft else  \" without PEFT\"))\n","print(f\"Accuracy:{sum(q)/len(q):.2f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fSjt3zxlbZ01","executionInfo":{"status":"ok","timestamp":1692294470807,"user_tz":-180,"elapsed":408,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"55ceb9da-f9ba-4b05-834e-fcda9b4a59a8"},"id":"fSjt3zxlbZ01","execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["model_name_or_path='google/flan-t5-base'\n","num_epochs=3\n","elapsed_time=93.63 seconds with PEFT\n","Accuracy:0.87\n"]}]},{"cell_type":"markdown","source":[],"metadata":{"id":"FkeK06D3QbtW"},"id":"FkeK06D3QbtW"},{"cell_type":"code","source":[],"metadata":{"id":"Fnsz6AVImBDj"},"id":"Fnsz6AVImBDj","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Saving PEFT models"],"metadata":{"id":"wMiysPBaJx9Q"},"id":"wMiysPBaJx9Q"},{"cell_type":"code","execution_count":null,"id":"a8de6005","metadata":{"id":"a8de6005"},"outputs":[],"source":["# saving model\n","peft_model_path=\"my_lora_model\"\n","model.save_pretrained(peft_model_path)"]},{"cell_type":"code","source":["!ls -lh $peft_model_path"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wmYS-0zAPDIZ","executionInfo":{"status":"ok","timestamp":1685267344511,"user_tz":-180,"elapsed":5,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"d298cb76-0fb4-4af4-ded3-3d5f9eafc403"},"id":"wmYS-0zAPDIZ","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["total 3.5M\n","-rw-r--r-- 1 root root  332 May 28 09:49 adapter_config.json\n","-rw-r--r-- 1 root root 3.5M May 28 09:49 adapter_model.bin\n"]}]},{"cell_type":"markdown","source":["# Şuna Bir bakalimü\n","\n","merge filan"],"metadata":{"id":"n2cS0ZKWGfVB"},"id":"n2cS0ZKWGfVB"},{"cell_type":"code","source":["model = AutoPeftModelForCausalLM.from_pretrained(output_dir, device_map=\"auto\", torch_dtype=torch.bfloat16)\n","model = model.merge_and_unload()\n","\n","output_merged_dir = \"results/llama2/final_merged_checkpoint\"\n","os.makedirs(output_merged_dir, exist_ok=True)\n","model.save_pretrained(output_merged_dir, safe_serialization=True)\n","\n","# save tokenizer for easy inference\n","tokenizer = AutoTokenizer.from_pretrained(model_name)\n","tokenizer.save_pretrained(output_merged_dir)"],"metadata":{"id":"MkBiJyfDGe1t"},"id":"MkBiJyfDGe1t","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Loading Saved PEFT model"],"metadata":{"id":"NtC15V8ER5wb"},"id":"NtC15V8ER5wb"},{"cell_type":"code","execution_count":null,"id":"76c2fc29","metadata":{"id":"76c2fc29","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1685267379389,"user_tz":-180,"elapsed":513,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"436c2383-a49f-4089-e9e2-5496d52d12bd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["PeftConfig(peft_type='LORA', base_model_name_or_path='google/flan-t5-base', task_type='SEQ_2_SEQ_LM', inference_mode=True)"]},"metadata":{},"execution_count":46}],"source":["from peft import PeftModel, PeftConfig\n","config = PeftConfig.from_pretrained(peft_model_path)\n","config"]},{"cell_type":"code","source":["model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)\n","model = PeftModel.from_pretrained(model, peft_model_path)\n","model = model.merge_and_unload()\n","model.eval()"],"metadata":{"id":"0mWGEshUQ_4y"},"id":"0mWGEshUQ_4y","execution_count":null,"outputs":[]},{"cell_type":"code","source":["my_text= snli_sampled_dict[\"validation\"][\"text\"][0]\n","my_label= snli_sampled_dict[\"validation\"][\"label\"][0]\n","print(f\"{my_text=}\")\n","print(f\"{my_label=}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kqizWfRTRVx7","executionInfo":{"status":"ok","timestamp":1685267404981,"user_tz":-180,"elapsed":8,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"607dcecb-e17b-441c-a3f2-2140c2bb42b1"},"id":"kqizWfRTRVx7","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["my_text='S1:The young man wearing a blue sweatshirt and blue jeans is hopping over the railing on the fence. S2:The young man is hopping over the railing to save the young girl from drowning in the pool.. The relation between S1 and S2 is labeled as entailment, neutral or contradiction ?'\n","my_label='It is neutral'\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"Fx7RxxozSU0R"},"id":"Fx7RxxozSU0R","execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"id":"37d712ce","metadata":{"id":"37d712ce","outputId":"769a77de-6950-4313-cd69-50ec188000ea","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1685267449616,"user_tz":-180,"elapsed":507,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': tensor([[  180,   536,    10,   634,  1021,   388,  5119,     3,     9,  1692,\n","         10242,  9486,    11,  1692, 14118,    19,     3, 21714,   147,     8,\n","          6579,    53,    30,     8,  8227,     5,   180,   357,    10,   634,\n","          1021,   388,    19,     3, 21714,   147,     8,  6579,    53,    12,\n","          1097,     8,  1021,  3202,    45, 24614,    53,    16,     8,  2201,\n","             5,     5,    37,  4689,   344,   180,   536,    11,   180,   357,\n","            19,  3783,    15,    26,    38,     3,    35,  5756,   297,     6,\n","          7163,    42, 27252,     3,    58,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","         1, 1, 1, 1]])}\n"]}],"source":["inputs = tokenizer(my_text, return_tensors=\"pt\")\n","print(inputs)"]},{"cell_type":"code","source":["with torch.no_grad():\n","    outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n","    print(outputs)\n","    print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8HdajMpARJqU","executionInfo":{"status":"ok","timestamp":1685046175837,"user_tz":-180,"elapsed":596,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"bae6430e-f47c-46f8-d519-4d168075524b"},"id":"8HdajMpARJqU","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[   0,   94,   19, 7163,    1]])\n","['It is neutral']\n"]}]},{"cell_type":"markdown","source":["#Train with QLora"],"metadata":{"id":"Sln5kRJeucy8"},"id":"Sln5kRJeucy8"},{"cell_type":"code","source":["!pip install bitsandbytes"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gxcYXS_pu0xq","executionInfo":{"status":"ok","timestamp":1692294489691,"user_tz":-180,"elapsed":8121,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"9ce91773-78e3-47e9-ab87-2b7e78b53f1a"},"id":"gxcYXS_pu0xq","execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting bitsandbytes\n","  Downloading bitsandbytes-0.41.1-py3-none-any.whl (92.6 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: bitsandbytes\n","Successfully installed bitsandbytes-0.41.1\n"]}]},{"cell_type":"code","source":["from transformers import BitsAndBytesConfig\n","from peft import prepare_model_for_kbit_training\n","\n","from transformers import AutoModel\n","#model_name = \"daryl149/llama-2-7b-chat-hf\"\n","model_name = \"google/flan-t5-base\"\n","\n","\n","bnb_config = BitsAndBytesConfig(\n","    load_in_4bit=True,\n","    bnb_4bit_use_double_quant=True,\n","    bnb_4bit_quant_type=\"nf4\",\n","    bnb_4bit_compute_dtype=torch.bfloat16\n",")\n","\n","model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path,\n","                                              quantization_config=bnb_config,\n","                                              device_map={\"\":0})\n","\n","model.gradient_checkpointing_enable()\n","model = prepare_model_for_kbit_training(model)\n","model = get_peft_model(model, peft_config)"],"metadata":{"id":"euhmAZgluhpr","executionInfo":{"status":"ok","timestamp":1692294518190,"user_tz":-180,"elapsed":2017,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"id":"euhmAZgluhpr","execution_count":26,"outputs":[]},{"cell_type":"code","source":["model.print_trainable_parameters()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"w3DDIKpMxHk7","executionInfo":{"status":"ok","timestamp":1692294520587,"user_tz":-180,"elapsed":485,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"7d6ca322-9dd5-427f-b5d9-73f9cfd8cbc0"},"id":"w3DDIKpMxHk7","execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 884,736 || all params: 248,462,592 || trainable%: 0.3560841867092814\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"OzKqzTmb5Zxg","executionInfo":{"status":"ok","timestamp":1692294522284,"user_tz":-180,"elapsed":5,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"id":"OzKqzTmb5Zxg","execution_count":27,"outputs":[]},{"cell_type":"code","source":["device=\"cuda\"\n","model = model.to(device)\n","num_epochs = 3\n","\n","# optimizer and lr scheduler\n","optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n","lr_scheduler = get_linear_schedule_with_warmup(\n","    optimizer=optimizer,\n","    num_warmup_steps=0,\n","    num_training_steps=(len(train_dataloader) * num_epochs),\n",")\n","\n","import time\n","st = time.time()\n","for epoch in range(num_epochs):\n","    model.train()\n","    total_loss = 0\n","    for step, batch in enumerate(tqdm(train_dataloader)):\n","        batch = {k: v.to(device) for k, v in batch.items()}\n","        outputs = model(**batch)\n","        loss = outputs.loss\n","        total_loss += loss.detach().float()\n","        loss.backward()\n","        optimizer.step()\n","        lr_scheduler.step()\n","        optimizer.zero_grad()\n","\n","    model.eval()\n","    eval_loss = 0\n","    eval_preds = []\n","    for step, batch in enumerate(tqdm(eval_dataloader)):\n","        batch = {k: v.to(device) for k, v in batch.items()}\n","        with torch.no_grad():\n","            outputs = model(**batch)\n","        loss = outputs.loss\n","        eval_loss += loss.detach().float()\n","        eval_preds.extend(\n","            tokenizer.batch_decode(\n","                torch.argmax(outputs.logits, -1)\\\n","                .detach().cpu().numpy(),\n","                skip_special_tokens=True)\n","        )\n","    eval_loss_avg = eval_loss / len(eval_dataloader)\n","    train_loss_avg = total_loss / len(train_dataloader)\n","    print(f\"{epoch=}-> {train_loss_avg=}\\t {eval_loss_avg=}\")\n","et = time.time()\n","elapsed_time = et - st"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"04PBY7eruhY_","executionInfo":{"status":"ok","timestamp":1692294652105,"user_tz":-180,"elapsed":128439,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"1c4bacd9-9732-4889-81da-52c05b282f0b"},"id":"04PBY7eruhY_","execution_count":28,"outputs":[{"output_type":"stream","name":"stderr","text":["  0%|          | 0/121 [00:00<?, ?it/s]`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n","100%|██████████| 121/121 [00:37<00:00,  3.21it/s]\n","100%|██████████| 52/52 [00:05<00:00, 10.19it/s]\n"]},{"output_type":"stream","name":"stdout","text":["epoch=0-> train_loss_avg=tensor(0.3169, device='cuda:0')\t eval_loss_avg=tensor(0.1001, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 121/121 [00:37<00:00,  3.22it/s]\n","100%|██████████| 52/52 [00:05<00:00, 10.17it/s]\n"]},{"output_type":"stream","name":"stdout","text":["epoch=1-> train_loss_avg=tensor(0.1098, device='cuda:0')\t eval_loss_avg=tensor(0.0825, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 121/121 [00:37<00:00,  3.21it/s]\n","100%|██████████| 52/52 [00:05<00:00, 10.19it/s]"]},{"output_type":"stream","name":"stdout","text":["epoch=2-> train_loss_avg=tensor(0.0958, device='cuda:0')\t eval_loss_avg=tensor(0.0808, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}]},{"cell_type":"code","source":["zipped=zip(eval_preds, snli_sampled_dict[\"validation\"][\"label\"])\n","q=[real.strip() in pred.strip() for pred,real in zipped]\n","print(f\"{model_name_or_path=}\")\n","print(f\"{num_epochs=}\")\n","print(f\"{elapsed_time=:.2f} seconds\"\n","     + (\" with PEFT\" if with_peft else  \" without PEFT\"))\n","print(f\"Accuracy:{sum(q)/len(q):.2f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"waRFLRBqw7ph","executionInfo":{"status":"ok","timestamp":1692294652105,"user_tz":-180,"elapsed":20,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"35fb3396-d38f-41a3-be7d-aa4a848dbaca"},"id":"waRFLRBqw7ph","execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["model_name_or_path='google/flan-t5-base'\n","num_epochs=3\n","elapsed_time=128.33 seconds with 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